In order to control the error rate and the ratio of error types on synthetic data, we apply the same processing method to each sentence in One Billion Word Benchmark monolingual corpus [1]}. This corpus consist of nearly one billion words of English taken from news articles on the web. We corrupt each token \(t\)  with a fixed probability \(E_{rate}\) . The operations of corruption include, deleting the current token \(t\)  with a probability of \(p_m\)  (Missing error type), inserting a random token \(t^{\prime }\)  in the vocabulary \(V\)  to the left of \(t\)  with a probability of \(p_u\)  (Unnecessary error type) and replacing \(t\)  with a random token \(t^{\prime }\)  in \(V\)  with a probability of \(p_r\)  (Replacement error type). Each operation corresponds to an error in this sentence pair. When the replaced token \(t \in \mathbb {P}=\lbrace ^{\prime }\ \ "\ \ ,\ \ .\ \ !\ \ ?\rbrace \) , it can be only replaced by the elements in \(\mathbb {P}\) . Algorithm REF  formalizes this approach. The vocabulary \(V\)  is a collection of all different tokens in the monolingual and labeled corpora.
